The accurate identification of target imagery in the presence of sea fog is essential for the precise detection and comprehension of targets situated at sea. To overcome the issues encountered when applying traditional polarimetric dehazing methods to sea fog imagery, this paper proposes an improved polarimetric dehazing method. Initially, the methodology employs quartile-based selection on polarization difference images to ascertain atmospheric light at an infinite distance. Subsequently, the study describes a segmentation approach for sea–sky background images based on the degree of polarization. The results show that the image information entropy of the segmentation process improves by more than 6% compared to that of alternative methodologies, and the local contrast of the image is increased by more than 30% compared to that of the original foggy image. These outcomes confirm the effectiveness of the proposed dehazing methodology in addressing the challenges associated with sea fog imagery.

Polarized 3D imaging technology reconstructs the three-dimensional (3D) surface shape of an object by analyzing the polarization characteristics of light reflected from its surface. A key challenge in polarized 3D imaging is accurately estimating the zenith angle. Specular light poses a notable challenge in estimating the zenith angle because it conveys limited information regarding the target. To enhance the accuracy and robustness of zenith angle estimation for specular light, this study proposes a novel zenith angle estimation method that utilizes both specular and diffuse reflections. Based on the estimated zenith angle, the target surface shape was reconstructed. The feasibility of the proposed method was validated using polarimetric images of marine targets, offering a new solution for the accurate identification and 3D imaging of distant maritime targets.

To address the issue of reduced image contrast and visibility caused by turbid media, such as dense fog, this paper proposes a novel polarization-based single-image dehazing model. The model introduces a first-of-its-kind nonlinear joint polarization model for airlight and target light. This model is established within a Cartesian coordinate system, abstracted as an analytical geometric model. Leveraging the structural similarity principle in images, boundary constraints are applied to enhance the accuracy of target light estimation. Finally, image dehazing and enhancement are achieved using the atmospheric scattering model. Experimental results demonstrate that the proposed algorithm does not rely on dataset training, maintains the highest structural consistency, and achieves superior image restoration across various scenarios, producing results that most closely resemble natural observation.